PODCAST · education
Welcome to CSC108 Machine Learning for All
by Welcome to CSC108 Machine Learning for All
This course introduces the fundamentals of machine learning (ML) with a focus on applications in social sciences. Students will learn key concepts, methods, and tools for analyzing and interpreting data, enabling them to apply machine learning techniques to real-world social science problems. Key ML algorithms like learned decision trees, logistic regression, artificial neural networks will be introduced in this course. Datasets used for the ML instruction and practice will be drawn from social justice issues. Students will carry out ML experiments with real datasets. The hands-on experiments will use low code software like Orange Data Mining, Weka, and Blockly-DS to increase student understanding of the typical elements of the ML workflow. Equipped with this understanding, the concepts of the Python programming language and supporting tools like programming notebooks and ML libraries in Python will be introduced so students are exposed to how ML software is developed in the industry.
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Machine Learning For All
Explore how machine learning fundamentals empower analysis in social sciences, from key algorithms to real-world dataset experimentation. Professor Michael Thompson takes you through accessible tools, essential ML concepts, and their transformative impact on social justice research.
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ABOUT THIS SHOW
This course introduces the fundamentals of machine learning (ML) with a focus on applications in social sciences. Students will learn key concepts, methods, and tools for analyzing and interpreting data, enabling them to apply machine learning techniques to real-world social science problems. Key ML algorithms like learned decision trees, logistic regression, artificial neural networks will be introduced in this course. Datasets used for the ML instruction and practice will be drawn from social justice issues. Students will carry out ML experiments with real datasets. The hands-on experiments will use low code software like Orange Data Mining, Weka, and Blockly-DS to increase student understanding of the typical elements of the ML workflow. Equipped with this understanding, the concepts of the Python programming language and supporting tools like programming notebooks and ML libraries in Python will be introduced so students are exposed to how ML software is developed in the industry.
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Welcome to CSC108 Machine Learning for All
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